A Study on the Optimization of Fire Awareness Model Based on Convolutional Neural Network: Layer Importance Evaluation-Based Approach 


Vol. 13,  No. 9, pp. 444-452, Sep.  2024
https://doi.org/10.3745/TKIPS.2024.13.9.444


PDF
  Abstract

This study proposes a deep learning architecture optimized for fire detection derived through Layer Importance Evaluation. In order to solve the problem of unnecessary complexity and operation of the existing Convolutional Neural Network (CNN)-based fire detection system, the operation of the inner layer of the model based on the weight and activation values was analyzed through the Layer Importance Evaluation technique, the layer with a high contribution to fire detection was identified, and the model was reconstructed only with the identified layer, and the performance indicators were compared and analyzed with the existing model. After learning the fire data using four transfer learning models: Xception, VGG19, ResNet, and EfficientNetB5, the Layer Importance Evaluation technique was applied to analyze the weight and activation value of each layer, and then a new model was constructed by selecting the top rank layers with the highest contribution. As a result of the study, it was confirmed that the implemented architecture maintains the same performance with parameters that are about 80% lighter than the existing model, and can contribute to increasing the efficiency of fire monitoring equipment by outputting the same performance in accuracy, loss, and confusion matrix indicators compared to conventional complex transfer learning models while having a learning speed of about 3 to 5 times faster.

  Statistics
  Cite this article

[IEEE Style]

W. Jin and M. Song, "A Study on the Optimization of Fire Awareness Model Based on Convolutional Neural Network: Layer Importance Evaluation-Based Approach," The Transactions of the Korea Information Processing Society, vol. 13, no. 9, pp. 444-452, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.9.444.

[ACM Style]

Won Jin and Mi-Hwa Song. 2024. A Study on the Optimization of Fire Awareness Model Based on Convolutional Neural Network: Layer Importance Evaluation-Based Approach. The Transactions of the Korea Information Processing Society, 13, 9, (2024), 444-452. DOI: https://doi.org/10.3745/TKIPS.2024.13.9.444.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals